PROJECT TITLE :

Semi-Supervised Image Dehazing

ABSTRACT:

The dehazing of a single image can now be accomplished using a semi-supervised learning system that we've developed. A deep Convolutional Neural Network (CNN) with supervised and unsupervised learning branches is used in the approach. Deep neural networks are bound by the supervised loss functions, which include mean squared, perceptual, and adversarial losses, when working in the supervised section. It's in the unsupervised branch that we use image attributes like light channel sparsity and gradient priors in the network. " An end-to-end training approach is used to develop the suggested network using both synthetic and real-world data. Analysis of the proposed semi-supervised learning technique indicates that it is not confined to synthetic training datasets and can be easily generalised to real-world photos. On benchmark datasets as well as real-world photos, the proposed approach outperforms the current state-of-the-art single image dehazing algorithms.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Systematic Clinical Evaluation of a Deep Learning Method for Medical Image Segmentation Radiosurgery Application ABSTRACT: We conduct an in-depth analysis of a Deep Learning model by using it to segment three-dimensional
PROJECT TITLE : On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks ABSTRACT: To fully realize the potential of deep learning in histopathology applications, a bottleneck
PROJECT TITLE : Multi-Magnification Image Search in Digital Pathology ABSTRACT: This study proposes the use of multi-magnification image representation and investigates the effect that magnification has on content-based image
PROJECT TITLE : Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation ABSTRACT: The long-term goal of image restoration and manipulation is to acquire a solid understanding of image priors. Existing
PROJECT TITLE : Learning Deformable Image Registration from Optimization Perspective, Modules, Bilevel Training and Beyond ABSTRACT: The goal of conventional deformable registration methods is to solve an optimization model that

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry